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Fanny Yang: Surprising failures of standard practices in ML when the sample size is small.
Fanny Yang

Sat Dec 03 01:30 PM -- 01:55 PM (PST) @
in I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification »

In this talk, we discuss two failure cases of common practices that are typically believed to improve on vanilla methods: (i) adversarial training can lead to worse robust accuracy than standard training (ii) active learning can lead to a worse classifier than a model trained using uniform samples. In particular, we can prove both mathematically and empirically, that such failures can happen in the small-sample regime. We discuss high-level explanations derived from the theory, that shed light on the causes of these phenomena in practice.

Author Information

Fanny Yang (ETH Zurich)

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